Application of Computer Vision and Image Processing in Medicine
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 1238
Special Issue Editor
Special Issue Information
Dear Colleagues,
This journal aims to highlight a wide range of interdisciplinary research and practical innovations situated at the intersection of medical science, computer vision, artificial intelligence, and biomedical engineering.
In the field of medical imaging, computer vision focuses on developing automated techniques to interpret, analyse, and extract clinically relevant insights from medical images. Leveraging advances in AI, machine learning, Big Data, and image processing, these approaches support various clinical tasks such as diagnosis, treatment planning, disease monitoring, and image-guided interventions.
The scope of the journal includes but is not limited to
- Medical image analysis;
- AI and deep learning applications in medical imaging;
- Self-supervised learning techniques for medical imaging;
- The use of transformers in medical image interpretation;
- Multimodal medical image analysis (e.g., X-Ray, CT, MRI, PET);
- Image quality enhancement and restoration;
- Traditional image processing methods for preprocessing (e.g., denoising, enhancement);
- Telemedicine and point-of-care imaging solutions;
- Evaluation and benchmarking of imaging algorithms;
- Ethical, regulatory, and societal considerations in AI for healthcare;
- Responsible AI and governance of medical imaging data;
- Image reconstruction and processing techniques;
- Image-guided therapies and interventions;
- Diagnostic and prognostic imaging systems;
- Computer-aided diagnosis (CAD) tools;
- Surgical and interventional imaging technologies;
- Image registration and multimodal data fusion;
- Human–AI collaboration in clinical decision support
- Generative AI for synthetic medical image creation and augmentation;
- Large vision models (LVMs) adapted for medical imaging;
- Synthetic data generation for training medical AI systems;
- End-to-end AI pipelines for precision imaging
Dr. Armaghan Moemeni
Guest Editor
Manuscript Submission Information
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Keywords
- medical imaging
- medical image analysis
- deep learning
- CNN
- transfomers
- imaging modalities (MRI, CT, PET)
- large vision model (LVM), self-supervised learning
- precision imaging
- AI for health
- intervension
- diagnosis
- CAD
- early diagnosis
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